Contrast enhancement has an important role in image processing applications. Various contrast enhancement techniques have been described in the literature, such as (a) gray-level transformation based techniques (e.g., logarithm transformation, power-law transformation, piecewise-linear transformation, etc.) and (b) histogram processing techniques (e.g., histogram equalization (HE), histogram specification, etc.) Conventional contrast enhancement techniques may yield acceptable results if the proper technique is selected for a given application along with the proper processing parameters. However, conventional contrast enhancement techniques often fail in producing satisfactory results for a broad range of low-contrast images, such as images characterized by the fact that the amplitudes of their histogram components are very high at one or several locations on the grayscale, while they are very small, but, not zero, in the rest of the grayscale. This makes it difficult to increase the image contrast by simply stretching its histogram or by using simple gray-level transformations. The high amplitude of the histogram components corresponding to the image background also often prevents the use of the histogram equalization techniques, which could cause a washed-out effect on the appearance of the output image and/or amplify the background noise. FIG. 1A illustrates an example of a low-contrast image and FIGS. 2A and 3A illustrate the results of treating that image with conventional contrast enhancement techniques.
FIG. 1A shows an original low-contrast image of the Mars moon, Phobos. The histogram of gray-level values of the image in FIG. 1A is presented in FIG. 1B. FIG. 2A depicts the results of the application of a conventional histogram equalization image processing technique to the image of FIG. 1A. FIG. 2A exhibits a washed-out appearance which is not acceptable for many applications. The cause of the washed-out appearance is that the left half of the grayscale on the histogram of the equalized image is simply empty, as shown in FIG. 2B.
FIG. 3A illustrates the results of the application of a conventional histogram specification image processing technique applied to the image of FIG. 1A. FIG. 3B presents the gray level histogram of the image in FIG. 3A, (which is better than the histogram equalization result FIG. 2A), but still has an unsatisfactory appearance. More importantly, one major disadvantage of the histogram specification technique is that the desired histogram of the result image has to be specified manually, and this precludes the technique from being applied automatically. The manually specified desired histogram used in the treatment is depicted in FIG. 3C.
Various other histogram-based contrast enhancement techniques have been developed, but most of them are derivatives of the previously-noted conventional techniques such as bi-histogram equalization, block-overlapped histogram equalization, multi-scale adaptive histogram equalization, shape preserving local histogram modification, and so on. The mean brightness of histogram-equalized image is always the middle gray-level regardless of the input mean, and this is undesirable in certain applications where brightness preservation is necessary. This characteristic of HE may also lead to a washed-out appearance, amplified noise or other annoying artifacts in the resulting image. Bi-histogram equalization (BHE) was proposed to preserve the brightness by separating the input image's histogram into two parts based on its mean—one ranges from the minimum gray-level value to the mean gray level, the other from the mean to the maximum. The two histograms are then equalized independently. Equal Area Dualistic Sub-Image Histogram Equalization (DSIHE) is similar to BHE except that DSIHE separates the histogram at the median gray-level value—the gray-level value with cumulative probability equal to 0.5 instead of the mean. These two techniques usually outperform the basic histogram equalization (HE) technique. However, they have the same limitations of HE and cannot enhance some images well, as they still perform the HE operation in each grayscale segment, just limiting the drawbacks of HE within each grayscale segment.
Global histogram equalization methods have also been developed, but typically they cannot adapt to local brightness features of the input image because it uses histogram information over the whole image. This fact limits the contrast-stretching ratio in some parts of the image, and causes significant contrast losses in the background and other small regions. To overcome this limitation, some local histogram-equalization methods have been developed. A natural extension of global histogram equalization is referred to as adaptive histogram equalization (AHE), which divides the input image into an array of sub-images, each sub-image is histogram-equalized independently, and then the processed sub-images are fused together with bilinear interpolation.
Another local method is called block-overlapped histogram equalization, in which a rectangular sub-image of the input image is first defined, a histogram of that block is obtained, and then its histogram-equalization function is determined. Thereafter, the center pixel of the block is histogram equalized using this function. The center of the rectangular block is then moved to the adjacent pixel and the histogram equalization is repeated. This procedure is repeated pixel by pixel for all input pixels. Since local histogram equalization is generally performed for all pixels in the entire image, the computation complexity of this method is very high. Instead of using rectangular blocks, shape preserving local histogram modification employs connected components and level-sets for contrast enhancement. Multi-scale adaptive histogram equalization and other multi-scale contrast enhancement techniques use multi-scale analysis to decompose the image into sub-bands, and apply corresponding enhancement techniques to the high-frequency sub-band, and then combine the enhanced high-frequency sub-band with the low-frequency sub-band to reconstruct the output image.
Some of the advanced contrast enhancement techniques described above may outperform simpler techniques. However, they still have limitations and cannot handle certain classes of images well and/or are not fully automatic methods. What are needed therefore are improved techniques for image enhancement that cover broader ranges of image problems and that may be implemented as automated processes.